We Continue the Work of Those
Who Were the First.

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Current issue

ELEKTRO 7/2018 was released on June 27th 2018. Its digital version will be available on July 27th 2018.

Topic: Cables, conductors and cable engineering; Tools, equipment and accessories for work with cables

Main Article
Parametrization of circuit models of Li-accumulators for electromobility
Smart Cities (part 3 – volume 1)

SVĚTLO (Light) 4/2018 was released on July 30th 2018. Its digital version will be available on August 31th 2018.

Refreshing our memory
Eccentric luminaires of René Roubíček from the years1965 till 1977
Bases of photometry – 1st part
Great personage of Czech science of times after Battle at Bílá hora: doctor, naturalist, philosopher and physicist Jan Marek Marci from Kronland

Optical radiation effects and use
The light and circadian rhythms

Getting More Miles From Plug-in Hybrids

17.02.2016 | University of California | ucrtoday.ucr.edu

Plug-in hybrid electric vehicles (PHEVs) can reduce fuel consumption and greenhouse gas emissions compared to their gas-only counterparts. Researchers at the University of California, Riverside’s Bourns College of Engineering have taken the technology one step further, demonstrating how to improve the efficiency of current PHEVs by almost 12 percent.

Since plug-in hybrids combine gas or diesel engines with electric motors and large rechargeable batteries, a key component is an energy management system (EMS) that controls when they switch from ‘all-electric’ mode, during which stored energy from their batteries is used, to ‘hybrid’ mode, which utilizes both fuel and electricity. As new EMS devices are developed, an important consideration is combining the power streams from both sources in the most energy-efficient way.

Better efficiency of hybrid systems

While the UCR EMS does require trip-related information, it also gathers data in real time using onboard sensors and communications devices, rather than demanding it upfront. It is one of the first systems based on a machine learning technique called reinforcement learning (RL).

In comparison-based tests on a 20-mile commute in Southern California, the UCR EMS outperformed currently available binary mode systems, with average fuel savings of 11.9 percent. Even better, the system gets smarter the more it’s used and is not model- or driver-specific, meaning it can be applied to any PHEV driven by any individual.

Read more at University of California

Image Credit: Wikipedia

-jk-